phewas_meta_logic = function(a, fixed=T, keep.both=T, ...){
  #Get information about the current step
  phenotype=as.character(a$phenotype[1])
  snp=as.character(a$snp[1])
  adjustment=as.character(a$adjustment[1])
  #Remove NA results from the input
  a=a[!is.na(a$p),]
  #Get type (may have been NA for one row)
  type=as.character(a$type[1])
  #If there are no records left, return an NA row (so one still knows it was there)
  if(nrow(a)==0) {
    #create an NA row to return.
    c=data.frame(phenotype=phenotype,snp=snp,adjustment=adjustment,
                 beta=NA_real_, OR=NA_real_,SE=NA_real_,p=NA_real_,type=NA_character_,
                 n_total=sum(a$n_total,na.rm=TRUE),
                 n_cases=sum(a$n_cases,na.rm=TRUE),
                 n_controls=sum(a$n_controls,na.rm=TRUE),
                 HWE_p.min=NA_real_,allele_freq=NA_real_,
                 n_no_snp=NA_integer_,k_studies=0,tau2=NA_real_,I2.percent=NA_real_,
                 Q=NA_real_,Q.df=NA_real_,Q.p=NA_real_,beta.fixed=NA_real_,OR.fixed=NA_real_,
                 SE.fixed=NA_real_,p.fixed=NA_real_,beta.random=NA_real_,
                 OR.random=NA_real_,SE.random=NA_real_,p.random=NA_real_, stringsAsFactors=F)
  } else {#If there was at least one good analysis, calculate the meta-analysis
    #Define the Odds Ratio summary measure if appropriate
    sm=""
    if(type=="logistic") {
      sm="OR"
    } 
    #Warn if the type is not linear or logistic
    if(!type %in% c("logistic","linear")) {
      warning(paste0("No match for study type ",type,". Assuming no summary measure for metagen."))
    }
    #Throw an error if study types, e.g. logistic and linear, don't match
    if(length(na.omit(unique(a$type)))>1) stop(paste0("Study types do not match for ",phenotype,", ",snp,", ",adjustment,"."))
    #Perform the meta analysis.
    b=metagen(TE=a$beta,seTE=a$SE, sm=sm,n.e=a$n_cases,n.c=a$n_controls,
              studlab=a$study, title=paste0(phenotype," ",snp," ",adjustment), ...)
    #Calculate I2, setting to 0 if below 0
    I2=(b$Q-b$df.Q)/b$Q*100
    I2=ifelse(I2>=0,I2,0)
    #Calculate the total N
    n_total=sum(b$n.e,b$n.c)
    #Create the data frame with the meta analysis results
    c=data.frame(phenotype=phenotype,snp=snp,adjustment=adjustment,
                 beta=NA_real_,
                 OR=NA_real_,
                 SE=NA_real_,
                 p=NA_real_,
                 type=type,
                 n_total=n_total,
                 n_cases=sum(b$n.e),
                 n_controls=sum(b$n.c),
                 HWE_p.min=min(a$HWE_p),
                 allele_freq=sum(a$allele_freq*a$n_total)/n_total,
                 n_no_snp=sum(a$n_no_snp),
                 k_studies=b$k,
                 tau2=b$tau^2,
                 I2.percent=I2,
                 Q=b$Q,
                 Q.df=b$df.Q,
                 Q.p=pchisq(b$Q,b$df.Q,lower.tail=FALSE),
                 beta.fixed=b$TE.fixed,
                 OR.fixed=exp(b$TE.fixed),
                 SE.fixed=b$seTE.fixed,
                 p.fixed=b$pval.fixed,
                 beta.random=b$TE.random,
                 OR.random=exp(b$TE.random),
                 SE.random=b$seTE.random,
                 p.random=b$pval.random, stringsAsFactors=F
    )
    
    #Set OR measures to NA if the summary measure is not Odds Ratio
    if(sm!="OR") {
      c$OR.fixed=NA_real_
      c$OR.random=NA_real_
    }
  }
  #Set the default attributes based on user preference
  if(fixed) {
    c$beta=c$beta.fixed
    c$OR=c$OR.fixed
    c$SE=c$SE.fixed
    c$p=c$p.fixed
  } else {
    c$beta=c$beta.random
    c$OR=c$OR.random
    c$SE=c$SE.random
    c$p=c$p.random
  }
  #remove the fixed/random specific attributes if requested.
  if(!keep.both) {
    c=c[,-21:-28]
  }
  c
}